Enhancing Parkinson's Disease Diagnosis Through Mayfly-Optimized CNN BiGRU Classification

Abstract

Neuron degeneration in the human brain causes Parkinson's disease (PD). The technique employed for detecting early PD is based on a parametric analysis of acoustic sounds. Machine learning methods such as SVM, KNN, decision tree, and random forest are used in a significant manner. When compared to machine learning, deep-learning (DL) algorithms always produce greater results. DL algorithms are utilized to assess the patient's voice record. In this work, the authors introduce the MAYFLY optimization algorithm, a nature-inspired metaheuristic optimization technique. The approach utilizes convolutional neural networks (CNNs) coupled with bi-directional gated recurrent units (BiGRUs) to leverage the temporal and spatial features of biomedical data. Experimental evaluations reveal that the proposed approach produces better accuracy of 98.31% and better precision rate (98.78%). These results demonstrate the potential of the MAYFLY-based CNN-BiGRU model in differentiating PD patients from healthy individuals.